{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IMSPM - IMmersed Solid-Phase Microextraction\n",
    "Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#First import numpy, since all arrays are based on numpy\n",
    "import numpy as np \n",
    "\n",
    "#Import matplotlib, for plotting\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import the main software library\n",
    "import simex_lib as simex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All parameters must be adopted in the same units of space/time/mass/volume\n",
    "here we adopt:\n",
    "- space units: centimeters (cm) \n",
    "- time units: seconds (s)\n",
    "- mass: Micrograms (mug)\n",
    "- volume: cubic-centimeters or milliliters (cm^3, mL)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Experiment basename (for filenames and figures)\n",
    "name = \"imspme\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step is to set the domain, that is, the position of the interfaces and boundaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Domain definition (position of interfaces) \n",
    "# x0   x1   x2  x3 \n",
    "# |----|----|---|\n",
    "#x=np.array([initial_position, interface_1, final_position])\n",
    "x=np.array([0, 0.2, 0.25]) # cm "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we set the names of the compartments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Names of compartments\n",
    "#xnames = [\"\", \"\"]\n",
    "xnames = [\"sample\", \"spme fiber\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set initial concentrations, constant in each compartment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Initial concentrations in each space\n",
    "#  Assumed constant for each space\n",
    "#   C0   C1    \n",
    "# |----|----|\n",
    "#C=np.array([C0, C1])   # (mug/mL)\n",
    "C=np.array([1, 0.0]) # (mug/mL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diffusion coefficients for each compartment, constant at each one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Diffusion coefficients for each space\n",
    "#   D0   D1    \n",
    "# |----|----|\n",
    "#D=np.array([D0, D1])           # (cm^2/s)\n",
    "D=np.array([18e-6, 2.8e-6]) # (cm^2/s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the partition coefficients. Always enforce zero at external boundaries for no flow condition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Partition coefficients K for each interface\n",
    "# Set 0.0 for beggining and endpoints    \n",
    "# K0  K1  K2\n",
    "# |---|---|\n",
    "#K=np.array([K0, K1, K2]) # 2 boundaries and 1 interface coefficient, non-dimensional\n",
    "K=np.array([0.0, 0.1, 0.0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now configure runtime options for this simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Max time definition (how long to run the simulation)\n",
    "maxtime = 14400 # seconds\n",
    "\n",
    "#Time step size\n",
    "dt = 0.1  # seconds - depending on your diffusion coefficients, this may be required to be small\n",
    "\n",
    "#Plotting time spots (snapshots of the concentrations are saved in these time instants)\n",
    "iplot_time=np.array([0.0, 600, 1800, 3600, 7200, 14400]) # seconds\n",
    "#iplot_time=np.linspace(0, 50, 21, endpoint=True) #Equally spaced plots, for animations\n",
    "\n",
    "#Space discretization (number of grid points) - depending on your device, this may need to be incresed\n",
    "N = 500"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now build the device with these parameters (we have given the same names of the variables as the device function arguments, but we could have called the variables differently)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "        --------------------------------------------------------------\n",
      "        Simex - Simulation of Extraction Processes\n",
      "        --------------------------------------------------------------\n",
      "        \n",
      "You defined a device with 2 compartment(s).\n",
      "Mechanism layout/interfaces (x):  [0.   0.2  0.25]\n",
      "Initial concentrations: [1. 0.]\n",
      "Diffusion coefficients: [1.8e-05 2.8e-06]\n",
      "Interface coefficients: [0.  0.1 0. ]\n",
      "Output basename: output/imspme/imspme\n",
      "\n",
      "Compartment 0  setup\n",
      " Name:                   sample\n",
      " Local Domain:           [0.  0.2]\n",
      " Difusion (neigbours):   [0.0e+00 1.8e-05 2.8e-06]\n",
      " Border/Interfaces Coef: [0.  0.1]\n",
      "\n",
      "Compartment 1  setup\n",
      " Name:                   spme fiber\n",
      " Local Domain:           [0.2  0.25]\n",
      " Difusion (neigbours):   [1.8e-05 2.8e-06 0.0e+00]\n",
      " Border/Interfaces Coef: [0.1 0. ]\n",
      "\n",
      "Proposed number of control volumes (grid points):  500\n",
      "Adjusted number of grid points:  500\n",
      "Number of degrees of freedom:  497\n",
      "\n",
      "\n",
      "Time-space info (dx, dt, Nt, maxD, dx/maxD):\n",
      "0.0005 0.1 144000 1.8e-05 27.77777777777778\n",
      "\n",
      "Equilibrium concentrations:\n",
      " [0.28571428571428575, 2.857142857142857]\n",
      "------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "p = simex.device(\n",
    "        D = D, \n",
    "        K = K, \n",
    "        C = C, \n",
    "        xspace = x, \n",
    "        xnames = xnames, \n",
    "        name = name, \n",
    "        N = N, dt = dt, maxtime = maxtime, iplot_time = iplot_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "Now that the device structure is built, we can actually use in a few ways. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets loop in time the model to see how the concentrations evolve in time. The main function here is run(), which calculates all time steps of an implicit solver for the model of the device. It will save and return snapshots at all time set in iplot_time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " It:  0  Time:  0.0  Mass:  0.20215  %Dif Eq:  250.0000 %\n",
      " It:  6000  Time:  600.0  Mass:  0.20009  %Dif Eq:  153.1699 %\n",
      " It:  18000  Time:  1800.0  Mass:  0.19986  %Dif Eq:  31.7249 %\n",
      " It:  36000  Time:  3600.0  Mass:  0.19981  %Dif Eq:  2.8340 %\n",
      " It:  72000  Time:  7200.0  Mass:  0.19980  %Dif Eq:  0.1790 %\n",
      " It:  144000  Time:  14400.0  Mass:  0.19980  %Dif Eq:  0.1714 %\n",
      " \n",
      " Saving concentrations as \n",
      "  output/imspme/imspme_data.csv\n",
      "\n",
      " %Eq   Time \n",
      "50.0 %   1454.80\n",
      "60.0 %   1624.20\n",
      "70.0 %   1842.30\n",
      "80.0 %   2149.10\n",
      "90.0 %   2670.90\n",
      "95.0 %   3186.40\n",
      "99.0 %   4318.00\n",
      "99.5 %   4742.10\n",
      "\n"
     ]
    }
   ],
   "source": [
    "concentrations = p.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The snapshots were saved in the csv files and also returned to the list of snapshots given at \"concentrations\". We can use this output to plot the concentrations, as follows. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 1, constrained_layout=True, figsize=(15,10))\n",
    "plt.xlabel(\"x-distance (cm)\")\n",
    "plt.ylabel(\"concentration ($\\mu g/mL$)\")\n",
    "plt.title(\"IMSPME Example\")\n",
    "\n",
    "for i, c in enumerate(concentrations):    \n",
    "    #Plot\n",
    "    t = iplot_time[i]\n",
    "    axes.plot(p.x, c, label=str(t)+\"s\")\n",
    "    \n",
    "for i, name in enumerate(xnames):\n",
    "    xlabel=0.5*p.xspace[i]+0.5*p.xspace[i+1]\n",
    "    plt.text(xlabel, -0.05, name)\n",
    "    \n",
    "axes.plot(p.x, p.u_equi_ext, 'k--', label=\"Theoretical \\n Equilibrium\")\n",
    "axes.legend()\n",
    "plt.savefig(p.basename+\"_final.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}